目前在人工嗅覺領域中，利用電子鼻系統對混合氣體作有效的分析與識別仍是一大課題。當化學感測器陣列同時截取到多種目標氣體及當下環境的資訊時，如何採取適當的訊號處理使電子鼻系統能對抗不同的干擾，例如環境變量，背景氣體變化，感測器飄移等等，以獲取有意義的多變量響應作為特徵進行氣味識別(分類)和濃度估計(回歸)的任務，藉此增加整個系統的強健性，為本論文所關注的重點。首先，針對過去常採用電阻變化比例並取其穩態值作為特徵，本論文提出以電導變化差來進行比較，並且採用指數移動平均的概念在訊號未達穩態時擷取暫態響應，用以描述感測器對氣體的反應速率，使資料的分類鑑別度增加。接著在辨識混合氣味時，本論文採用基於構成成分作決策的多標籤辨識方法，有別於過去將單一組合皆視為一類進行分類，此方法分別對數種已知構成成分建立二元決策模型，最後將所有決策結果綜合，即為混合氣體辨識結果。與傳統方式相比，除了有效減低分類器負擔外，在低維度時也有更高的正確率。最後，為了進一步預估混合氣體各成分的濃度估計，除了使用多元線性回歸分析外，為避免資料產生共線性問題，本論文運用偏最小平方法(PLS)進行預估，此方法也可套用在分類問題上。結果顯示以電導作為特徵時，針對兩種氣體混合的情況，也能有效估計出其濃度。

In current research of artificial olfaction, effective analysis and recognition of mixed odors is still a challenging issue. Considering the specific case of a gas sensor array exposed to multiple target gases and various background gases simultaneously, and the sensor data are subject to interference from the environment such as temperature and humidity. In this research, we intend to find out some signal processing methods to get meaningful responses as multivariate features for odor identification (classification) and concentration estimation (regression). First, the thesis compares the performance with conductance difference changes and resistance fractional changes, the results show that using of conductance can achieve better accuracy rates. Secondly, we use transient features together with steady-state features. The underlying idea is that the transient phase may include additional information concerning to the constituting gases that the steady state does not provide. For the odor identification tasks, we perform multi-label classification using so-called the Individual Constituent Decision Method (ICDM) instead of conventional multi-class classification. The results show that multi-label classification reduces the computational complexity and improves the recognition accuracy. For the concentration estimation tasks, Multiple Linear Regression (MLR) is the common approach to estimate the concentration of individual constituents. In addition, in order to avoid the collinearity problem, this thesis uses the Partial Least Squares (PLS) method to estimate the concentration, and this method can also be applied to the classification problem. The results show that we can reasonably estimate the concentration of the individual constituent with conductance responses when two analyte gases existed simultaneously.

目錄
摘要 i
Abstract ii
誌謝 iii
第一章 緒論 1
1.1 生物嗅覺機制 1
1.2 電子鼻系統介紹 2
1.3 研究方向與文獻探討 3
第二章 實驗裝置與資料擷取流程 6
2.1 實驗設計 6
2.2 實驗方法與流程 8
2.3 氣體響應訊號前處裡 10
2.3.1 基線操作(Baseline Operation) 10
2.3.2 暫態壓縮(Transient Compression) 13
2.3.3 正規化(Normalization) 14
第三章 多變量資料的識別與回歸分析 15
3.1 特徵萃取演算法(Feature Extraction) 15
3.1.1 主成分分析法(Principal Component Analysis, PCA) 15
3.1.2 線性識別分析法(Linear Discrimination Analysis, LDA) 16
3.2 線性多變量分析(Linear Multivariate Analysis) 18
3.2.1 多元線性回歸(Multiple Linear Regression, MLR) 19
3.2.2 偏最小平方法(Partial Least Squares, PLS) 20
3.2.3 回歸分析效能評估 22
3.3 分類器(Classifier) 23
3.3.1 K個最鄰近分類器(K-nearest neighbor classifier, KNN) 23
3.3.2 支持向量機(Support Vector Machine, SVM) 24
3.4 混合氣體辨識方法 26
3.4.1 多類別辨識方法(Multi-Class Classification) 26
3.4.2 多標籤辨識方法(Multi-Label Classification) 27
3.4.3 交叉驗證(Cross-Validation) 29
第四章 結果分析與討論 30
4.1 混合氣體成分辨識分析 32
4.1.1 特徵萃取方法比較 33
4.1.2 加入暫態響應的特徵萃取結果 38
4.1.3 分類器模型比較 42
4.1.4 比較多標籤與多類別辨識方法 44
4.2 混合氣體濃度回歸分析 46
第五章 結論與未來工作 49
參考文獻 51
附錄 56
A.1結合濃度預估與混合氣體辨識方法 56

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